Advocacy Coalition Framework and Policy Changes in a Third‐World Country
Bibliographic record
Abstract
This work concerns health sector reform in Nigeria between 2003 and 2014. Using qualitative content analysis, I investigate the factors that led to the reform, the reform process, and its outcome. The objective is to assess the conformity of the policy reform to Sabatier and Weible's Advocacy Coalition Framework (ACF) in order to ascertain the applicability, or otherwise, of the ACF in policy analysis in stable democratic African states. In the article, external shock, policy subsystem, stable parameter, technical experts, advocacy coalitions, the “devil shift,” and policy core beliefs—which are basic components of ACF—were all identified in the policy reform process. I conclude that the ACF can be applied in policy reform in democratic African states, so long as there is the rule of law, a separation of powers, freedom of speech and association, a fairly stable political environment, and the presence of policy participants with expert knowledge of the policy issues. Related Articles Morris, Mary Hallock. 2007. “The Political Strategies of Winning and Losing Coalitions: Agricultural and Environmental Groups in the Debate over Hypoxia.” Politics & Policy 35 (4): 836‐871. https://doi.org/10.1111/j.1747-1346.2007.00086.x Swigger, Alexandra, and Bruce Timothy Heinmiller. 2014. “Advocacy Coalitions and Mental Health Policy: The Adoption of Community Treatment Orders in Ontario.” Politics & Policy 42 (2): 246‐270. https://doi.org/10.1111/polp.12066 Udjo, Eric O., and Barney Erasmus. 2014. “Impact of Retirement Age Policy on the Workforce of a Higher Education Institution in South Africa.” Politics & Policy 42 (5): 744‐768. https://doi.org/10.1111/polp.12092 Related Media CNBC Africa. 2017. “How Can Nigeria Improve Its Healthcare System?” https://www.cnbcafrica.com/videos/2017/09/14/how-can-nigeria-improve-its-healthcare-system/ Opeyemi, Agbaje. 2012. “Health Sector Reforms in Nigeria 1.” https://www.youtube.com/watch?v=0YvV_xj0meU
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".